Engineering a large IP backbone network without an accurate, network-wide view of the tra c demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in signi cant (and sudden) uctuations in load. In this paper, we present a model of tra c demands to support tra c engineering and performance debugging of large Internet Service Provider networks. By de ning a tra c demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing a ects the tra c traveling between domains. To infer the tra c demands, we propose a measurement methodology that combines ow-level measurements collected at all ingress links with reachability information about all egress links. We d i scuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Speci cally, we show how to infer interdomain tra c demands using measurements collected at a smaller number of edge links | the peering links connecting to neighboring providers. We report on our experiences in deriving the tra c demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, con guration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the tra c demands and a discussion of the practical implications for tra c engineering.
Managing large IP networks requires an understanding of the current tra c ows, routing policies, and network con guration. Yet, the state-of-the-art for managing IP networks involves manual con guration of each IP router, and tra c engineering based on limited measurements. The networking industry is sorely lacking in software systems that a large Internet Service Provider (ISP) can use to support tra c measurement and network modeling, the underpinnings of e ective tra c engineering. This paper describes the AT&T Labs NetScope, a uni ed set of software tools for managing the performance of IP backbone networks.The key idea behind NetScope is to generate global views of the network, on the basis of con guration and usage data associated with the individual network elements. Having created an appropriate global view, we are able to infer and visualize the network-wide implications of local changes in tra c, con guration, and control. Using NetScope, a network provider can experiment w i t h c hanges in network con guration in a simulated environment, rather than the operational network. In addition, the tool provides a sound framework for additional modules for network optimization and performance debugging. We demonstrate the capabilities of the tool through an example tra c-engineering exercise of locating a heavily-loaded link, identifying which tra c demands ow on the link, and changing the con guration of intra-domain routing to reduce the congestion.
In his seminal paper on probabilistic Turing machines, Gill [9] asked whether the class PP is closed under intersection and union. We give a positive answer to this question. In fact, PP is closed under polynomial-time multilineal reductions. In circuits, this allows us to combine several threshold gates into a single threshold gate, while increasing depth by only a constant.
We prove upper and lower bounds on the competitiveness of randomized algorithms for the list update problem of Sleator and Tarjan. We give a simple and elegant randomized algorithm that is more competitive than the best previous randomized algorithm due to Irani. Our algorithm uses randomness only during an initialization phase, and from then on runs completely deterministically. It is the first randomized competitive algorithm with this property to beat the deterministic lower bound. We generalize our approach to a model in which access costs are fixed but update costs are scaled by an arbitrary constant d. We prove lower bounds for deterministic list update algorithms and for randomized algorithms against oblivious and adaptive on-line adversaries. In particular, we show that for this problem adaptive on-line and adaptive off-line adversaries are equally powerful.
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